Causal Disentanglement-Based Hidden Markov Model for Cross-Domain Bearing Fault Diagnosis

作者：Rihao Chang, Yongtao Ma, Weizhi Nie, Jie Nie, Yiqun Zhu, An-An Liu · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2024 · DOI：10.1109/tnnls.2024.3513329 · 被引用次数：12 · 研究领域：Fault Detection and Control Systems、Machine Fault Diagnosis Techniques

In the predictive maintenance of modern industries, accurate fault diagnosis under complex conditions is now a major research focus. Recent research has demonstrated the effectiveness of deep learning in advancing bearing fault diagnosis. However, due to the scarcity of industrial failure data, achieving robust generalization in complex working conditions remains a challenge. To address this, we propose the causal disentanglement-based hidden Markov model (CDHM), which is designed to recognize the underlying causality in bearing vibration signals, capturing essential fault patterns for a more accurate and generalizable fault representation. Compared to signal-processing methods, deep learning approaches bypass the complex signal analysis, yet overlook the significance of signal theories in precise fault diagnosis. Nevertheless, the bearing vibration mechanism sheds light on the fact that the vibration induced by a certain type of fault has a consistent pattern across different system conditions, while the fault-irrelevant vibration such as noise and interference varies. Therefore, the CDHM constructs a time-series structural causal model (SCM), offering a new perspective on the interconnections of bearing vibration signals. Based on the SCM, a hidden Markovian variational autoencoder (VAE) is designed to progressively disentangle the vibration signal into two parts: a fault-relevant representation capturing essential bearing fault characteristics, and a fault-irrelevant repre...